code: def FixedQuantize(
      inputs,
      init_min=-6.0,
      init_max=6.0,
      scope=None,
      narrow_range=False,
  ):
      """Adds a fake quantize layer with fixed quantization interval.
  
      Args:
        inputs: a tensor containing values to be quantized.
        init_min: the lower end of quantization interval.
        init_max: the upper end of quantization interval.
        scope: Optional scope for name_scope.
        narrow_range: Whether to use the narrow quantization range
          [1; 2^num_bits - 1] or wide range [0; 2^num_bits - 1].
      Returns:
        a tensor containing quantized values.
      """
      if scope is None:
          scope = "FixedQuantize"
  
      with tf.name_scope(scope):
          return tf.quantization.fake_quant_with_min_max_args(
              inputs,
              min=init_min,
              max=init_max,
              narrow_range=narrow_range,
          )
  
file_path: model-optimization/tensorflow_model_optimization/python/core/quantization/keras/quant_ops.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import compat
label_desc: Use image node_id 1 for calling a global function with example usage: FixedQuantize(inputs, init_min, init_max, scope, narrow_range) and returns: tf